Modern medical diagnostics are assisted to a significant extent by medical imaging which, for example, is based on radiological, angiographic, sonographic and/or tomographic image records of patients. However, evaluating such medical image records by a radiologist was often found to require much outlay. Thus, in order to identify spinal column tumors, the radiologist needs to, in general, examine each individual vertebra imaged in a record of the spinal column. Thus, automatic identification, detection and/or evaluation of anatomical features in medical image records by using image analysis methods is increasingly sought after in order to assist such a diagnosis.
To this end, the use of image analysis methods based upon machine learning is known. These often have a machine classifier, e.g. a so-called random forest classifier, which is specific to the lesions to be identified. However, such classifiers generally require a large training set with manually annotated boundary regions around the lesions to be identified. However, the provision of such manually annotated training sets is very complicated. Moreover, such classifiers often do not take into account a larger visual context about a lesion to be identified in each case, e.g. on the scale of a whole vertebra or the entire spinal column. By way of example, such an image analysis method based on machine classifiers is known from the article “Multi-stage osteolytic spinal bone lesion detection from CT data with internal sensitivity control” by M. Wels, B. M. Kelm, A. Tsymbal et al., in SPIE Med Imaging Proc 8315 (2012).